22 citations · 45 across the 27 of their papers we have counts for
5 papers · 1 filter
Flex-PE: Flexible and SIMD Multi-Precision Processing Element for AI Workloads
Mukul Lokhande, Gopal Raut, Santosh Kumar Vishvakarma
The rapid adaptation of data driven AI models, such as deep learning inference, training, Vision Transformers (ViTs), and other HPC applications, drives a strong need for runtime p…
HYDRA: Hybrid Data Multiplexing and Run-time Layer Configurable DNN Accelerator
Sonu Kumar, Komal Gupta, Gopal Raut +2
Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes H…
SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI
Narendra Singh Dhakad, Yuvnish Malhotra, Santosh Kumar Vishvakarma +1
This paper introduces a Scalable Hierarchical Aware Convolutional Neural Network (SHA-CNN) model architecture for Edge AI applications. The proposed hierarchical CNN model is metic…
HOAA: Hybrid Overestimating Approximate Adder for Enhanced Performance Processing Engine
Omkar Kokane, Prabhat Sati, Mukul Lokhande +1
This paper presents the Hybrid Overestimating Approximate Adder designed to enhance the performance in processing engines, specifically focused on edge AI applications. A novel Plu…
Configurable Multi-Port Memory Architecture for High-Speed Data Communication
Narendra Singh Dhakad, Santosh Kumar Vishvakarma
Memory management is necessary with the increasing number of multi-connected AI devices and data bandwidth issues. For this purpose, high-speed multi-port memory is used. The tradi…